A Federated Learning-enabled Smart Street Light Monitoring Application: Benefits and Future Challenges

TL;DR

This paper evaluates federated learning for smart street light monitoring in IoT-enabled cities, demonstrating its privacy benefits and communication efficiency with minimal impact on classification accuracy.

Contribution

It assesses the feasibility of federated learning in smart city IoT applications, highlighting its advantages over centralized and fully personalized models.

Findings

FL reduces communication costs significantly.

FL maintains high classification accuracy.

FL enhances privacy preservation.

Abstract

Data-enabled cities are recently accelerated and enhanced with automated learning for improved Smart Cities applications. In the context of an Internet of Things (IoT) ecosystem, the data communication is frequently costly, inefficient, not scalable and lacks security. Federated Learning (FL) plays a pivotal role in providing privacy-preserving and communication efficient Machine Learning (ML) frameworks. In this paper we evaluate the feasibility of FL in the context of a Smart Cities Street Light Monitoring application. FL is evaluated against benchmarks of centralised and (fully) personalised machine learning techniques for the classification task of the lampposts operation. Incorporating FL in such a scenario shows minimal performance reduction in terms of the classification task, but huge improvements in the communication cost and the privacy preserving. These outcomes strengthen FL's viability and potential for IoT applications.